Firms often engage Financial & operational modeling consultants for several reasons, such as to better understand their own operations, as well as provide more accurate projections about future business endeavors. Modeling is a way to explore different possibilities for the organization. We explore both Financial & Operational Modeling below.
What is Operational modeling?
Operational modeling is a process that involves the translation of an organization’s activities into mathematical form in order to simulate them over time. While the most common process for achieving this is with linear programming techniques, other techniques include stochastic modeling and game theory. Operational modeling has been used extensively in fields such as military logistics, airport management, and finance, but it can be applied to any industry which relies heavily on a system of production.
Simple linear programming models, sometimes called logistical models, are often used for operations such as scheduling the delivery of goods. In this context, a firm can be thought of as a “central planner” that decides how many planes to dispatch in order to deliver goods from one location to another. Linear programming is most appropriate when the number of planes and destinations is limited.
Stochastic modeling is a mathematical technique for modeling events that may be random, or whose probability of occurring can’t be accurately predicted. For example, this technique could be used to model demand for a product where the sales volume depends on factors like competing products or economic conditions.
Game theory uses the mathematical formalism of probability theory to explore strategic situations. For example, a business may use game theory to analyze the data in a demand curve for their product, where one curve can be used for each specific customer. In this way, a firm could better understand how much it costs to supply a product and whether there is profit in doing so.
The limitations of these techniques are that the process by which they are used can easily become too complicated, thereby being unable to represent real-world problems. In order to achieve greater accuracy and precision in models, Fintalent’s financial & operational modeling experts employ more complex techniques such as Simpler Product Demand Models (SPDM).
In order for a firm to effectively use operational modeling, it is important that the modelers have the correct data and knowledge about how the firm operates. For example, if a firm is interested in improving its planning process, it is necessary to have accurate information on things such as production rate and capacity. In addition, it may be necessary to use data that has been collected from previous years in order to develop a repeatable process.
A firm “makes” money in three ways:
1) Selling products or services
3) Making money by borrowing. We look at these in the models that follow.
Also, the goal of financial modeling is to find out how we can make money by investing (or upgrading), and not by borrowing, because this is how all kinds of companies lose money. If firms were able to make more than their cost of capital in interest and dividends, then they would be able to have a lot of investment. This would mean that they could get more productive, deliver better customer service, build new plants, hire new people etc. If we are able to raise more capital (usually through borrowing) to invest in business operations, then we can grow.
One way to begin thinking about modeling firms is to ask yourself if you really understand what a company does and how it makes money. It might also be helpful to think about whether you have thought about your own job in this manner before.
Profits and losses can be thought of as the money that flows in or out of the firm. When you make a profit, you are spending less than you took in from revenue. If you spend more than you take in from revenue, then your profits are negative. For example: if sales revenue were $100 and the cost of goods sold is $100, this means that there was a profit of $100 or a loss of $100. You can find out about costs by using standard costing methods.
If you want to know whether you can afford to make any investment, then you need to know the return on assets (ROA) and the return on equity (ROE). ROA looks at the profit as a percentage of assets. ROE looks at the profit as a percentage of equity. The figures below show how we can use Excel models to show what happens with different types of strategies. The models are not intended to predict the future, but they will provide insights into what might happen under various circumstances.
Factors that Affect the Accuracy of Financial and Operational models
a) Limited or no historical data on past activities. If there is little or no historical data available on operations which may affect future projections, then those projections will likely be inaccurate.
b) Volume of activity. The volume of the activity determines the amount of dat to be processed and the likelihood of errors being encountered.
c) Production time. The amount of time it takes to complete production can vary with changes in the volume of activity, which may have a significant impact on project cost and objectives. For example, if an extra hour is added each day to a given project due to increased volume, then that cumulative effect will need to be accounted for in the model. Projections about when a product will be finished must also take into account vacations and holidays, where the effects are likely multiplied by the number of people involved in that given task.
d) Complexity and interaction among processes. For example, a project manager may need to know the production rate and capacity of several different departments in order to assess the most efficient way of scheduling delivery.
Another consideration is whether a firm can use modeling to improve performance. A firm could choose to invest in modeling in an attempt to gain a competitive advantage by improving its ability to predict demand patterns, thereby allowing it more flexibility in planning for future activities. By understanding how much an activity will cost, where the dollar value will be spent and where it can be spared is crucial information for a manager, as well as his or her decision making process regarding capital and resources allocation. Furthermore, operational models can also provide strategic insight by helping managers plan and evaluate similar projects at other companies.
While operational modeling has many benefits, it can also produce inaccurate results if data is not used accurately. Inaccurate data is often the result of subjective estimates or assumptions that may be incorrect. For example, a cost estimating model may make an estimate based on a simple linear regression formula without taking into account the effect of other factors that are not measurable, such as scale changes in production processes.
When errors occur in an operational model due to poor data, the consequences can be costly and unpredictable. For example, if the level of sales by an accounting department is underestimated and actual results are multiplied by 2.5, then there will be a $250,000 shortfall in revenue for that department. While a sudden drop in revenue may not directly impact the firm, it could cause other departments to start budgeting for smaller amounts of resources. The repercussions of a model error can be indirect, as well. For example, an inaccurate demand forecast for a product could lead to production delays, which have ripple effects that significantly impact the company’s goals.
As long as the amount of data is limited or nonexistent, there will always be some error associated with any calculation used to model a process because there are always factors that cannot be accounted for by simple mathematical formulas or computer algorithms.
The value of forecasting is dependent on the accuracy of the result. A company that is able to produce accurate predictions can plan with greater precision and certainty, thereby allowing it to focus more on evaluating and adapting to changes in demand patterns.